The present invention relates generally to a Smart Controller which is an impulse-response adaptive control system; or more particularly to a logical network which when interfaced with a mechanical system or an electromechanical system, will cause the output variable of the system to closely follow any command signal received.
Model reference control algorithms are synthesized on the basis that the plant may be described by a transfer function with unknown coefficients. A basic assumption under which the stability of these algorithms is proven is that the order of the plant transfer function be known.
The uncertainty in the coefficients or parameter values of a transfer function of a given order may be referred to as structured uncertainty. Most practical systems have high frequency plant dynamics which cannot be described by transfer functions of a given order for any values of the parameters. These high frequency dynamics are called unmodelled or unstructured dynamics. Unmodelled dynamics affect the performance of model reference adaptive control systems in a peculiar way. Recent studies (C. E. Rohrs et al, "Robustness of Continuous-Time Adaptive Control Algorithms in the Presence of Unmodelled Dynamics" IEEE Transactions on Automatic Control, Vol AC-30, pp 881-889, Sept. 1985) show that in the presence of unmodelled dynamics, a model reference adaptive control system becomes unstable if the reference input contains a high frequency sinusoid. Instability also occurs if there is a sinusoidal output disturbance at any frequency including d.c. The latter poses a serious problem because sinusoidal disturbances are common and the problem cannot be alleviated by adding a low-pass filter at the output (Rohrs et al ibid.). A remedy for this problem is to add low frequency excitation of sufficient magnitude at the input (Rohrs et al ibid. and K. J. Astrom, "A Commentary on the C. E. Rohrs et al Paper . . ." IEEE Transactions on Automatic Control, Vol AC-30, pp 889, Sept. 1985). A recent study (J. Krause et al, "Robustness Studies in Adaptive Control" , Proceedings 22nd, IEEE Conference Decision Control, San Antonio, Tex. Dec. 14-16, 1983, pp 977-981) also deals with the frequency range and the amount of excitation required to stabilize the adaptive control system in the presense of unmodelled dynamics and output disturbances.
Many practical systems (for example, flight control systems) exist where persistent excitation in the input signal is undesirable. Thus the problem with unmodelled dynamics reduces the appeal or even precludes the application of adaptive control to such systems
R. E. Kalman ("Design of a Self-Optimizing Control System" Transactions of ASME, Vol 80, pp 468-478, Feb. 1958) mentioned the idea of modeling a plant in terms of an impulse response sequence in the design of a self-optimizing control system. However, Kalman did not succeed in developing an adaptive controller based on on-line identification of impulse-response sequence of an unknown system. In the work of Kalman, the idea of impulse-response sequence was abandoned, and his control algorithm computes the coefficients of the rational transfer function of the unknown plant via a modified least square filtering procedure, which requires lengthy and involved calculations. Kalman's machine requires a considerable amount of programmed computation. Furthermore, in Kalman's design the order of the plant must be known. Each machine is built for plants that are lower than certain order. The computation grows exponentially with the order of the plant. Because of the computation time involved, the usefulness of the Kalman machine is limited to low order plants and low-sampling frequency digitalization.
Control designs based on approximate impulse-response models have been developed See R. K Mehra et al, "Basic Research in Digital Stochastic Model Algorithmic Control" Technical Report AFWAL-TR-80-3125; and W. E. Larimore & S. Mahmood, "Basic Research on Adaptive Model Algorithmic Control", Technical Report AFWAL-TR-85-3113 (available from NTIS as AD-A168 016), both from Flight Dynamics Laboratory, Wright-Patterson Air Force Base, Ohio. An application of Model Algorithmic Control (MAC) described in the Larimore & Mahmood Report is presented in a technical report by J. V. Carroll and R. F. Gendron: "Vectored Thrust Digital Flight Control For Crew Escape", AFWAL-TR-85-3113, Vols I-IV, also from Flight Dynamics Laboratory (Vol I available from NTIS as AD-A166 580). Model Algorithmic Control (MAC), described in the Larimore & Mahmood Report assumes that the unit-impulse response sequence of the controlled system (the plant) is known. The control variable is then computed from the desired output and the unit-impulse response sequence. The Adaptive MAC requires that the unknown plant be operated open-loop and off-line initially for seven seconds to enable its unit-response be identified. The o impulse-response model is then used in the digital controller and the control loop is closed A (random) dither signal and a measurement noise of sufficient magnitude and variance are deliberately introduced and superimposed on the actual input and output to enable the repeated identification of the plant's impulse response model every seven seconds. Thus, the Adaptive MAC cannot help being contaminated by an artificially introduced dither signal at the input and measurement noise at the output and at the feedback
Wroblewski et al in U.S. Pat. No. 4,736,367 describe the control of a plurality of relay drivers by smart control devices using smart sensors. A driver and receiver circuit receives, interprets and converts signals from the smart control devices and smart sensors and a microcomputer supplies continuous and updated information to a display system indicative of the status of each control device and each sensor and its associated switch. In Shigemasa et al U.S. Pat. No 4,641,235 a process control apparatus is provided with process dependent switching between two different control modes. A first mode is used for steady state and a second mode is used when the process characteristics vary frequently. Process characteristics are selected by sampling. Kugath et al in U.S. Pat. No. 3,923,166 show a master-slave mechanical system where the output closely follows the input. Cameron et al in U.S. Pat. No. 4,279,013 teach an adaptive controller for machine processes and Kurakake in U.S. Pat. No. 4,338,659 shows controlling a machine tool in accordance with position error by making a comparison between commanded positional information and detected positional information.